DEA-C02 Exam Dumps | A data engineering team is responsible for processing a high volume of semi-structured JSON data ingested

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Question 138/149

A data engineering team is responsible for processing a high volume of semi-structured JSON data ingested daily into Snowflake. The ingestion process currently uses a single 'X-Large' virtual warehouse. During peak hours, the data loading latency increases significantly, impacting downstream reporting. The team is considering either scaling up to a '3X-Large' warehouse or scaling out by creating a multi- cluster warehouse with a minimum of 2 and a maximum of 4 'X-Large' clusters. Which of the following factors should be prioritized when making this decision to optimize performance, considering cost and concurrency requirements?

A. The anticipated number of concurrent data loading jobs and the ability of Snowflake to automatically distribute these jobs across multiple clusters.

B. The complexity of the JSON data and the potential for improved parsing performance by a larger, single warehouse due to increased memory.

C. The budget constraints and the higher per-second cost of a larger warehouse compared to the potential cost savings from reduced overall execution time.

D. The impact on other workloads sharing the same virtual warehouse, favoring scaling up to isolate the data loading process from other query activities.

E. The type of JSON processing used. If using SQL functions like PARSE JSON, scaling out is more beneficial than scaling up.

Correct Answer: A,C,E

Scaling out (multi-cluster warehouse) is generally better for concurrency because Snowflake can distribute data loading jobs across multiple clusters. Scaling up provides more resources for a single job, potentially improving its performance, but doesn't address concurrency as effectively. Cost is crucial because larger warehouses are more expensive per second. Finally, the PARSE_JSON function's ability to be parallelized across multiple clusters provides performance benefits.

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